Unit sourcing

ABSTRACT

A method for wireless communication, for example, accessing a wireless network via a network access node and determining whether the network access node is a frequented node or a non-frequented node based on an identifier for the network access node is provided. The method executes a modem function using a corresponding selected hypothesis having associated weights for each feature associated with the modem function, and being a unit sourced hypothesis or a crowd sourced hypothesis, each hypothesis corresponding to a modem function and including a plurality of features, state information and at least one trigger point. The method sends information, to a server, the information comprising a device identifier identifying the UE, the modem function, the selected hypothesis and associated weights, metrics for each feature and state information, if state information is available, in response to a trigger point being met when executing the modem function. Other aspects are provided.

CLAIM OF PRIORITY UNDER 35 U.S. § 119

The present Application for Patent claims priority to Provisional PatentApplication No. 62/368,772, entitled “Unit Sourcing” filed Jul. 29,2016, which is assigned to the assignee hereof and hereby expresslyincorporated by reference herein for all purposes.

BACKGROUND Field

The present disclosure relates generally to communication systems, andmore particularly, to methods and apparatus for post deployment tuningof a user equipment (UE).

Background

Wireless communication systems are widely deployed to provide varioustelecommunication services such as telephony, video, data, messaging,and broadcasts. Typical wireless communication systems may employmultiple-access technologies capable of supporting communication withmultiple users by sharing available system resources. Examples of suchmultiple-access technologies include code division multiple access(CDMA) systems, time division multiple access (TDMA) systems, frequencydivision multiple access (FDMA) systems, orthogonal frequency divisionmultiple access (OFDMA) systems, single-carrier frequency divisionmultiple access (SC-FDMA) systems, and time division synchronous codedivision multiple access (TD-SCDMA) systems.

These multiple access technologies have been adopted in varioustelecommunication standards to provide a common protocol that enablesdifferent wireless devices to communicate on a municipal, national,regional, and even global level. An example telecommunication standardis Long Term Evolution (LTE). LTE is a set of enhancements to theUniversal Mobile Telecommunications System (UMTS) mobile standardpromulgated by Third Generation Partnership Project (3GPP). LTE isdesigned to support mobile broadband access through improved spectralefficiency, lowered costs, and improved services using OFDMA on thedownlink, SC-FDMA on the uplink, and multiple-input multiple-output(MIMO) antenna technology.

Prior to deployment of UEs, algorithms are built into the modems,however such algorithms are inherently rigid. The algorithms typicallyuse only weighted linear averaging of parameters with the parameters andweights being fixed and not adaptable. The parameters and weights aredetermined based on data collected in labs and based on minimal fieldstudies. The parameters and weights are conservatively tuned to supportall devices and all markets. For example, the device specificconfigurations, capability and performance, such as antennaconfiguration, is not taken into consideration for tuning purposes.Performance variations of the UEs can vary based on the differentregions of the network and the specific experience that the UEencounters traversing these networks. Typically, the modem is tunedonce. The tuning does not consider the current active set of services inthe choice of the algorithm behavior. As such, there is a demand forpost deployment tuning of the modem algorithms.

SUMMARY

The following presents a simplified summary of one or more aspects inorder to provide a basic understanding of such aspects. This summary isnot an extensive overview of all contemplated aspects, and is intendedto neither identify key or critical elements of all aspects nordelineate the scope of any or all aspects. Its sole purpose is topresent some concepts of one or more aspects in a simplified form as aprelude to the more detailed description that is presented later.

According to an example, a method for wireless communications isprovided. The method includes accessing a wireless network via a networkaccess node; determining whether the network access node is a frequentednode or a non-frequented node based on an identifier for the networkaccess node; executing a modem function using a corresponding selectedhypothesis having associated weights for each feature associated withthe modem function, the selected hypothesis being a unit sourcedhypothesis if the network access node is determined to be a frequentednode and the selected hypothesis being a crowd sourced hypothesis if thenetwork access node is determined to be a non-frequented node, whereinthe selected hypothesis is one of a set of hypotheses stored on the UEwith each hypothesis corresponding to a modem function and including aplurality of features, state information and at least one trigger point;and sending information, to a server, the information comprising adevice identifier identifying the UE, the modem function, the selectedhypothesis and associated weights, metrics for each feature and stateinformation, if the state information is available, in response to atrigger point being met when executing the modem function.

The method may also include sending, to the server, a modem algorithmidentifier identifying a version of the set of hypotheses stored on theUE. The method may further include sending, to the server, a subset ofhypotheses that the UE can use wherein the subset contains lesshypotheses than the set of hypotheses stored on the UE. The method mayfurther include sending, to the server, a time indicator that the servercan store information received from the UE. The method may furtherinclude receiving, from the server, a selected unit sourced hypothesisand a selected crowd sourced hypothesis, with each selectioncorresponding to a modem function and including weights for each featureassociated with the modem function, wherein the selected unit sourcedhypothesis is based on information the UE sent to the server and theinformation being run through one or more learning algorithms and theselected crowd sourced hypothesis based on crowd sourced data. The UEmay select the unit sourced hypothesis when the network access node is afrequented node based on an identifier for the network access node andthe crowd sourced hypothesis when the network access node is anon-frequented node based on an identifier for the network access node.

In another aspect, a method for wireless communications is provided. Themethod includes receiving information, at a server from a user equipment(UE), the information comprising a device identifier, a modem functionexecuted by the UE, a selected hypothesis for the corresponding modemfunction, metrics for each feature associated with the correspondingmodem function and state information, if the state information isavailable, in response to a trigger point associated with the modemfunction being met when executing the modem function; storing, by theserver, the received information; running, by the server, one or morelearning algorithms based on the stored information to cluster theinformation and to select a unit sourced hypothesis and associatedweights for the corresponding modem function when the UE provided thestate information; running, by the server, one or more learningalgorithms based on crowd sourced information from other UEs to clusterthe information and to select a unit sourced hypothesis and associatedweights for the corresponding modem function when the state informationwas not provided by the UE; selecting, by the server, the unit sourcedhypothesis and associated weights based on the one or more learningalgorithms; running, by the server, one or more learning algorithmsbased on crowd sourced information from other UEs to cluster theinformation and to select a crowd sourced hypothesis and associatedweights for the corresponding modem function when the state informationwas provided by the UE; selecting, by the server, the crowd sourcedhypothesis and associated weights based on the one or more learningalgorithms; and sending, by the server to the UE, at least one of theselected unit sourced hypothesis and associated weights or the selectedcrowd sourced hypothesis and associated weights for the correspondingmodem function.

The method may also include receiving by the server from the UE, a modemalgorithm identifier indicating a version of a set of hypotheses storedon the UE and the selected unit sourced hypothesis and the selected unitsourced hypothesis are selected from the set of hypotheses stored on theUE. The method may further include receiving by the server from the UE,a subset of hypotheses that the UE can use and the and the selected unitsourced hypothesis and the selected unit sourced hypothesis are selectedfrom the subset of hypotheses stored on the UE, where the subset is lessthan a set of hypotheses stored on the UE. The method may furtherinclude employing neural networks and sending neural networksinformation for applying a selected hypothesis. The method may furtherinclude receiving, by the server from the UE, a time indicator that theserver can store information received from the UE when the UE used aselected unit sourcing hypothesis. The method may further includesending, by the server to the UE, state information associated with aselected unit sourced hypothesis when the server ran one or morelearning algorithms based on crowd sourced information when the stateinformation was not provided by the UE. The method may further includesending, by the server to the UE, at least one of state informationassociated with a selected unit sourced hypothesis and state informationassociated with a selected crowd sourced hypothesis.

In another example, an apparatus for wireless communications isprovided. The apparatus includes a transceiver, a memory configured tostore instructions and one or more processors communicatively couplewith the transceiver and the memory. The one or more processors areconfigure to execute instructions to: access a wireless network via anetwork access node; determine whether the network access node is afrequented node or a non-frequented node based on an identifier for thenetwork access node; execute a modem function using a correspondingselected hypothesis having associated weights for each featureassociated with the modem function, the selected hypothesis being a unitsourced hypothesis if the network access node is determined to be afrequented node and the selected hypothesis being a crowd sourcedhypothesis if the network access node is determined to be anon-frequented node, wherein the selected hypothesis is one of a set ofhypotheses stored on the UE with each hypothesis corresponding to amodem function and including a plurality of features, state informationand at least one trigger point; and send information, to a server, theinformation comprising a device identifier identifying the UE, the modemfunction, the selected hypothesis and associated weights, metrics foreach feature and state information, if the state information isavailable, in response to a trigger point being met when executing themodem function.

In another example, a server for wireless communications is provided.The server includes a transceiver, a memory configured to storeinstructions and one or more processors communicatively couple with thetransceiver and the memory. The one or more processors are configure toexecute instructions to: receive information, at the server from a userequipment (UE), the information comprising a device identifier, a modemfunction executed by the UE, a selected hypothesis for the correspondingmodem function, metrics for each feature associated with thecorresponding modem function and state information, if the stateinformation is available, in response to a trigger point associated withthe modem function being met when executing the modem function; store,by the server, the received information; run, by the server, one or morelearning algorithms based on the stored information to cluster theinformation and to select a unit sourced hypothesis and associatedweights for the corresponding modem function when the UE provided thestate information; run, by the server, one or more learning algorithmsbased on crowd sourced information from other UEs to cluster theinformation and to select a unit sourced hypothesis and associatedweights for the corresponding modem function when the state informationwas not provided by the UE; select, by the server, the unit sourcedhypothesis and associated weights based on the one or more learningalgorithms; run, by the server, one or more learning algorithms based oncrowd sourced information from other UEs to cluster the information andto select a crowd sourced hypothesis and associated weights for thecorresponding modem function when the state information was provided bythe UE; select by the server, the crowd sourced hypothesis andassociated weights based on the one or more learning algorithms; andsend, by the server to the UE, at least one of the selected unit sourcedhypothesis and associated weights or the selected crowd sourcedhypothesis and associated weights for the corresponding modem function.

In another example, a user equipment (UE) for wireless communications isprovided. The UE includes means for accessing a wireless network via anetwork access node; means for determining whether the network accessnode is a frequented node or a non-frequented node based on anidentifier for the network access node; means for executing a modemfunction using a corresponding selected hypothesis having associatedweights for each feature associated with the modem function, theselected hypothesis being a unit sourced hypothesis if the networkaccess node is determined to be a frequented node and the selectedhypothesis being a crowd sourced hypothesis if the network access nodeis determined to be a non-frequented node, wherein the selectedhypothesis is one of a set of hypotheses stored on the UE with eachhypothesis corresponding to a modem function and including a pluralityof features, state information and at least one trigger point; and meansfor sending information, to a server, the information comprising adevice identifier identifying the UE, the modem function, the selectedhypothesis and associated weights, metrics for each feature and stateinformation, if the state information is available, in response to atrigger point being met when executing the modem function.

In another example, a server for wireless communications is provided.The server includes means for receiving information, at a server from auser equipment (UE), the information comprising a device identifier, amodem function executed by the UE, a selected hypothesis for thecorresponding modem function, metrics for each feature associated withthe corresponding modem function and state information, if the stateinformation is available, in response to a trigger point associated withthe modem function being met when executing the modem function; meansfor storing, by the server, the received information; means for running,by the server, one or more learning algorithms based on the storedinformation to cluster the information and to select a unit sourcedhypothesis and associated weights for the corresponding modem functionwhen the UE provided the state information; means for running, by theserver, one or more learning algorithms based on crowd sourcedinformation from other UEs to cluster the information and to select aunit sourced hypothesis and associated weights for the correspondingmodem function when the state information was not provided by the UE;means for selecting, by the server, the unit sourced hypothesis andassociated weights based on the one or more learning algorithms; meansfor running, by the server, one or more learning algorithms based oncrowd sourced information from other UEs to cluster the information andto select a crowd sourced hypothesis and associated weights for thecorresponding modem function when the state information was provided bythe UE; means for selecting, by the server, the crowd sourced hypothesisand associated weights based on the one or more learning algorithms; andmeans for sending, by the server to the UE, at least one of the selectedunit sourced hypothesis and associated weights or the selected crowdsourced hypothesis and associated weights for the corresponding modemfunction.

In a further example, a non-transitory computer-readable medium storingexecutable code is provided. The code include code for accessing awireless network via a network access node; code for determining whetherthe network access node is a frequented node or a non-frequented nodebased on an identifier for the network access node; code for executing amodem function using a corresponding selected hypothesis havingassociated weights for each feature associated with the modem function,the selected hypothesis being a unit sourced hypothesis if the networkaccess node is determined to be a frequented node and the selectedhypothesis being a crowd sourced hypothesis if the network access nodeis determined to be a non-frequented node, wherein the selectedhypothesis is one of a set of hypotheses stored on the UE with eachhypothesis corresponding to a modem function and including a pluralityof features, state information and at least one trigger point; and codefor sending information, to a server, the information comprising adevice identifier identifying the UE, the modem function, the selectedhypothesis and associated weights, metrics for each feature and stateinformation, if the state information is available, in response to atrigger point being met when executing the modem function.

In another example, a non-transitory computer-readable medium storingexecutable code is provided. The code include code for receivinginformation, at a server from a user equipment (UE), the informationcomprising a device identifier, a modem function executed by the UE, aselected hypothesis for the corresponding modem function, metrics foreach feature associated with the corresponding modem function and stateinformation, if the state information is available, in response to atrigger point associated with the modem function being met whenexecuting the modem function; code for storing, by the server, thereceived information; code for running, by the server, one or morelearning algorithms based on the stored information to cluster theinformation and to select a unit sourced hypothesis and associatedweights for the corresponding modem function when the UE provided thestate information; code for running, by the server, one or more learningalgorithms based on crowd sourced information from other UEs to clusterthe information and to select a unit sourced hypothesis and associatedweights for the corresponding modem function when the state informationwas not provided by the UE; code for selecting, by the server, the unitsourced hypothesis and associated weights based on the one or morelearning algorithms; code for running, by the server, one or morelearning algorithms based on crowd sourced information from other UEs tocluster the information and to select a crowd sourced hypothesis andassociated weights for the corresponding modem function when the stateinformation was provided by the UE; code for selecting, by the server,the crowd sourced hypothesis and associated weights based on the one ormore learning algorithms; and code for sending, by the server to the UE,at least one of the selected unit sourced hypothesis and associatedweights or the selected crowd sourced hypothesis and associated weightsfor the corresponding modem function.

To the accomplishment of the foregoing and related ends, the one or moreaspects comprise the features hereinafter fully described andparticularly pointed out in the claims. The following description andthe annexed drawings set forth in detail certain illustrative featuresof the one or more aspects. These features are indicative, however, ofbut a few of the various ways in which the principles of various aspectsmay be employed, and this description is intended to include all suchaspects and their equivalents.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram illustrating an example of a wireless communicationssystem and an access network.

FIG. 2A is a diagram illustrating an example of a DL frame structure inLTE.

FIG. 2B is a diagram illustrating an example of DL channels within theDL frame structure in LTE.

FIG. 2C is a diagram illustrating an example of an UL frame structure inLTE.

FIG. 2D is a diagram illustrating an example of UL channels within theUL frame structure in LTE.

FIG. 3 is a diagram illustrating an example of an evolved Node B (eNB)and user equipment (UE) in an access network.

FIG. 4 is schematic diagram of a network architecture according to oneor more described aspects.

FIG. 5 is a flowchart of an aspect of a method of wireless communicationperformed on a user equipment to tune a modem according to one or moredescribed aspects.

FIG. 6 is a diagram illustrating an example of a hardware implementationfor a user equipment employing a processing system according to one ormore described aspects.

FIGS. 7A and 7B are a flowchart of an aspect of a method of wirelesscommunication performed on a unit sourcing server to tune a modemaccording to one or more described aspects.

FIG. 8 is a diagram illustrating an example of a hardware implementationfor a unit sourcing server employing a processing system according toone or more described aspects.

FIG. 9 is a diagram illustrating a Neural Network based approach forLTE-WiFi handover management according to one or more described aspects.

DETAILED DESCRIPTION

This disclosure generally relates to tuning a modem residing in a userequipment (UE) using a tuning component in the UE in conjunction with aunit sourcing server (USS), after deployment of the UE.

In one high-level aspect, a modem of a UE can be tuned, post deployment,by a USS in conjunction with a tuning component on the UE. By tuning themodem, the UE is able to improve the performance of the modem byimproving or optimizing one or more algorithms that are stored on theUE. The algorithms are used to perform modem functions on the UE. Forexample, the algorithm for deciding when to switch from WiFi to LTE canbe tuned to improve the timing of the switch from WiFi to LTE so that auser of the UE does not experience poor quality prior to the switch. Todo so, a selected hypothesis having associated weights is employed whena modem algorithm is executing a corresponding modem function. Thehypothesis is selected from a set of hypotheses that are stored on theUE. The hypothesis is an equation that has features and weights. Thefeatures are values associated with a modem function, such as WiFiReceived Signal Strength Indicator (RSSI) or UpLink (UL) packet loss.The weights adjusts the values of the features.

To select a hypothesis and the associated weights, information isgathered from the UE. The UE is able to gather information and improvethe “behavior” of the modem algorithms directly associated with theenvironment experienced by the UE. In response to a triggering point,the tuning component can send information associated with features forthe executed modem function to the USS. The trigger point is used togather metrics of features at a given time, e.g., a snapshot of thevalues of the features at the time. The trigger point can be when adetermination is made to perform a modem function (e.g., switch fromWiFi to LTE) or not to perform the modem function (e.g., not to switchfrom WiFi to LTE). By using the USS to store and process the data, theUE is able to reduce or minimize using the memory and processor of theUE.

The USS runs one or more learning algorithms using the informationreceived from the UE and/or using crowd sourced information The USS runsthe one or more learning algorithms to select a unit sourced hypothesisand associated weights and a crowd sourced hypothesis and associatedweights. The selected unit sourced hypothesis can be employed when theUE connects to a wireless network via a network access point that the UEhas used in a given time period. The selected crowd sourced hypothesiscan be employed when the UE connects to the wireless network via anetwork access point that the UE has not used in the given time period.The UE employs the selected hypothesis and the associated weights when acorresponding modem function is executed.

Such an approach combines the use of cloud computing and machinelearning to tune the behavior of a UE modem on an individual UE basis.Although the information/data sent from the UE to the USS can be usedfor crowd sourcing, the approach allows for unit sourcing so the modemalgorithms can be fine-tuned based on the network experiences of theparticular UE. Such an approach avoids the conservative modemconfigurations that are utilized in conventional approaches. Rather, themodem can be tuned based on data obtained by a given UE for frequentedroutes and can be tuned based on crowd sourced data when the UE travelsoutside of the frequented routes. In some implementations, the UE canemploy or run both a selected unit sourced hypothesis and a selectedcrowd sourced hypothesis with the UE arbitrating across both todetermine which hypothesis provides the better result. By using such anapproach, the modem can be tuned where the relevant parameters andrelative association in terms of determining trigger points is not wellunderstood prior to deployment. Thus, the parameters can be tuned postdeployment and can allow for more parameters to be included in makingthe hypotheses.

The detailed description set forth below in connection with the appendeddrawings is intended as a description of various configurations and isnot intended to represent the only configurations in which the conceptsdescribed herein may be practiced. The detailed description includesspecific details for the purpose of providing a thorough understandingof various concepts. However, it will be apparent to those skilled inthe art that these concepts may be practiced without these specificdetails. In some instances, well known structures and components areshown in block diagram form in order to avoid obscuring such concepts.

Several aspects of telecommunication systems will now be presented withreference to various apparatus and methods. These apparatus and methodswill be described in the following detailed description and illustratedin the accompanying drawings by various blocks, components, circuits,processes, algorithms, etc. (collectively referred to as “elements”).These elements may be implemented using electronic hardware, computersoftware, or any combination thereof. Whether such elements areimplemented as hardware or software depends upon the particularapplication and design constraints imposed on the overall system.

By way of example, an element, or any portion of an element, or anycombination of elements may be implemented as a “processing system” thatincludes one or more processors. Examples of processors includemicroprocessors, microcontrollers, graphics processing units (GPUs),central processing units (CPUs), application processors, digital signalprocessors (DSPs), reduced instruction set computing (RISC) processors,systems on a chip (SoC), baseband processors, field programmable gatearrays (FPGAs), programmable logic devices (PLDs), state machines, gatedlogic, discrete hardware circuits, and other suitable hardwareconfigured to perform the various functionality described throughoutthis disclosure. One or more processors in the processing system mayexecute software. Software shall be construed broadly to meaninstructions, instruction sets, code, code segments, program code,programs, subprograms, software components, applications, softwareapplications, software packages, routines, subroutines, objects,executables, threads of execution, procedures, functions, etc., whetherreferred to as software, firmware, middleware, microcode, hardwaredescription language, or otherwise.

Accordingly, in one or more example aspects, the functions described maybe implemented in hardware, software, or any combination thereof. Ifimplemented in software, the functions may be stored on or encoded asone or more instructions or code on a computer-readable medium.Computer-readable media includes computer storage media. Storage mediamay be any available media that can be accessed by a computer. By way ofexample, and not limitation, such computer-readable media can comprise arandom-access memory (RAM), a read-only memory (ROM), an electricallyerasable programmable ROM (EEPROM), optical disk storage, magnetic diskstorage, other magnetic storage devices, combinations of theaforementioned types of computer-readable media, or any other mediumthat can be used to store computer executable code in the form ofinstructions or data structures that can be accessed by a computer.

FIG. 1 is a diagram illustrating an example of a wireless communicationssystem 100, including one or more user equipments (UEs) 104 with each UE104 having a tuning component 180, a unit sourcing server 182 and acrowd sourcing server 184. The tuning component 180 and unit sourcingserver 182 are configured to tune a modem on a UE 104 after the UE 104is deployed, e.g., post deployment. The crowd sourcing server 184 can beused to assist in the tuning of the UE 104. The details of the operationand architecture associated with the tuning component 180 are discussedin more detail below with respect to FIGS. 4-8. The wirelesscommunications system (also referred to as a wireless wide area network(WWAN)) includes base stations 102, UEs 104, and an Evolved Packet Core(EPC) 160. The base stations 102 may include macro cells (high powercellular base station) and/or small cells (low power cellular basestation). The macro cells include eNBs. The small cells includefemtocells, picocells, and microcells.

The base stations 102 (collectively referred to as Evolved UniversalMobile Telecommunications System (UMTS) Terrestrial Radio Access Network(E-UTRAN)) interface with the EPC 160 through backhaul links 132 (e.g.,S1 interface). In addition to other functions, the base stations 102 mayperform one or more of the following functions: transfer of user data,radio channel ciphering and deciphering, integrity protection, headercompression, mobility control functions (e.g., handover, dualconnectivity), inter-cell interference coordination, connection setupand release, load balancing, distribution for non-access stratum (NAS)messages, NAS node selection, synchronization, radio access network(RAN) sharing, multimedia broadcast multicast service (MBMS), subscriberand equipment trace, RAN information management (RIM), paging,positioning, and delivery of warning messages. The base stations 102 maycommunicate directly or indirectly (e.g., through the EPC 160) with eachother over backhaul links 134 (e.g., X2 interface). The backhaul links134 may be wired or wireless.

The base stations 102 may wirelessly communicate with the UEs 104. Eachof the base stations 102 may provide communication coverage for arespective geographic coverage area 110. There may be overlappinggeographic coverage areas 110. For example, the small cell 102′ may havea coverage area 110′ that overlaps the coverage area 110 of one or moremacro base stations 102. A network that includes both small cell andmacro cells may be known as a heterogeneous network. A heterogeneousnetwork may also include Home Evolved Node Bs (eNBs) (HeNBs), which mayprovide service to a restricted group known as a closed subscriber group(CSG). The communication links 120 between the base stations 102 and theUEs 104 may include uplink (UL) (also referred to as reverse link)transmissions from a UE 104 to a base station 102 and/or downlink (DL)(also referred to as forward link) transmissions from a base station 102to a UE 104. The communication links 120 may use MIMO antennatechnology, including spatial multiplexing, beamforming, and/or transmitdiversity. The communication links may be through one or more carriers.The base stations 102/UEs 104 may use spectrum up to Y MHz (e.g., whereY=5, 10, 15, or 20 MHz) bandwidth per carrier allocated in a carrieraggregation of up to a total of Yx MHz (x=number of component carriers)used for transmission in each direction. The carriers may or may not beadjacent to each other. Allocation of carriers may be asymmetric withrespect to DL and UL (e.g., more or less carriers may be allocated forDL than for UL). The component carriers may include a primary componentcarrier and one or more secondary component carriers. A primarycomponent carrier may be referred to as a primary cell (PCell) and asecondary component carrier may be referred to as a secondary cell(SCell).

The wireless communications system may further include a Wi-Fi accesspoint (AP) 150 in communication with Wi-Fi stations (STAs) 152 viacommunication links 154 in a 5 GHz unlicensed frequency spectrum. Whencommunicating in an unlicensed frequency spectrum, the STAs 152/AP 150may perform a clear channel assessment (CCA) or Listen Before Talk (LBT)functionality prior to communicating in order to determine whether thechannel is available (e.g., generally, to avoid transmitting on achannel where another transmission is occurring, which would causeinterference).

The small cell 102′ may operate in a licensed and/or an unlicensedfrequency spectrum. When operating in an unlicensed frequency spectrum,the small cell 102′ may employ LTE and use the same 5 GHz unlicensedfrequency spectrum as used by the Wi-Fi AP 150. The small cell 102′,employing LTE in an unlicensed frequency spectrum, may boost coverage toand/or increase capacity of the access network. LTE in an unlicensedspectrum may be referred to as LTE-unlicensed (LTE-U), licensed assistedaccess (LAA), or MuLTEfire.

Base stations 102, UEs 104, APs 150, and STAs 152 may also operate inone or more shared frequency bands, such as according to GeneralAuthorized Access (GAA) in the 3.5 GHz band.

The EPC 160 may include a Mobility Management Entity (MME) 162, otherMMEs 164, a Serving Gateway 166, a Multimedia Broadcast MulticastService (MBMS) Gateway 168, a Broadcast Multicast Service Center (BM-SC)170, and a Packet Data Network (PDN) Gateway 172. The MME 162 may be incommunication with a Home Subscriber Server (HSS) 174. The MME 162 isthe control node that processes the signaling between the UEs 104 andthe EPC 160. Generally, the MME 162 provides bearer and connectionmanagement. All user Internet protocol (IP) packets are transferredthrough the Serving Gateway 166, which itself is connected to the PDNGateway 172. The PDN Gateway 172 provides UE IP address allocation aswell as other functions. The PDN Gateway 172 and the BM-SC 170 areconnected to the IP Services 176. The IP Services 176 may include theInternet, an intranet, an IP Multimedia Subsystem (IMS), a PS StreamingService (PSS), and/or other IP services. The BM-SC 170 may providefunctions for MBMS user service provisioning and delivery. The BM-SC 170may serve as an entry point for content provider MBMS transmission, maybe used to authorize and initiate MBMS Bearer Services within a publicland mobile network (PLMN), and may be used to schedule MBMStransmissions. The MBMS Gateway 168 may be used to distribute MBMStraffic to the base stations 102 belonging to a Multicast BroadcastSingle Frequency Network (MBSFN) area broadcasting a particular service,and may be responsible for session management (start/stop) and forcollecting eMBMS related charging information.

The base station may also be referred to as a Node B, evolved Node B(eNB), an access point, a base transceiver station, a radio basestation, a radio transceiver, a transceiver function, a basic serviceset (BSS), an extended service set (ESS), or some other suitableterminology. The base station 102 provides an access point to the EPC160 for a UE 104. Examples of UEs 104 include a cellular phone, a smartphone, a session initiation protocol (SIP) phone, a laptop, a personaldigital assistant (PDA), a satellite radio, a global positioning system,a multimedia device, a video device, a digital audio player (e.g., MP3player), a camera, a game console, a tablet, a smart device, a wearabledevice, or any other similar functioning device. The UE 104 may also bereferred to as a station, a mobile station, a subscriber station, amobile unit, a subscriber unit, a wireless unit, a remote unit, a mobiledevice, a wireless device, a wireless communications device, a remotedevice, a mobile subscriber station, an access terminal, a mobileterminal, a wireless terminal, a remote terminal, a handset, a useragent, a mobile client, a client, or some other suitable terminology.

FIG. 2A is a diagram 200 illustrating an example of a DL frame structurein LTE, which may be utilized for communications between the wirelesscommunication devices of FIG. 1, e.g., by one or more of base stations102 or 102′, UEs 104, APs 150, and/or STAs 152. FIG. 2B is a diagram 230illustrating an example of channels within the DL frame structure inLTE, which may be utilized for communications between the wirelesscommunication devices of FIG. 1. FIG. 2C is a diagram 250 illustratingan example of an UL frame structure in LTE, which may be utilized forcommunications between the wireless communication devices of FIG. 1.FIG. 2D is a diagram 280 illustrating an example of channels within theUL frame structure in LTE, which may be utilized for communicationsbetween the wireless communication devices of FIG. 1. Other wirelesscommunication technologies may have a different frame structure and/ordifferent channels. In LTE, a frame (10 ms) may be divided into 10equally sized subframes. Each subframe may include two consecutive timeslots. A resource grid may be used to represent the two time slots, eachtime slot including one or more time concurrent resource blocks (RBs)(also referred to as physical RBs (PRBs)). The resource grid is dividedinto multiple resource elements (REs). In LTE, for a normal cyclicprefix, an RB contains 12 consecutive subcarriers in the frequencydomain and 7 consecutive symbols (for DL, OFDM symbols; for UL, SC-FDMAsymbols) in the time domain, for a total of 84 REs. For an extendedcyclic prefix, an RB contains 12 consecutive subcarriers in thefrequency domain and 6 consecutive symbols in the time domain, for atotal of 72 REs. The number of bits carried by each RE depends on themodulation scheme.

As illustrated in FIG. 2A, some of the REs carry DL reference (pilot)signals (DL-RS) for channel estimation at the UE. The DL-RS may includecell-specific reference signals (CRS) (also sometimes called common RS),UE-specific reference signals (UE-RS), and channel state informationreference signals (CSI-RS). FIG. 2A illustrates CRS for antenna ports 0,1, 2, and 3 (indicated as R₀, R₁, R₂, and R₃, respectively), UE-RS forantenna port 5 (indicated as R₅), and CSI-RS for antenna port 15(indicated as R). FIG. 2B illustrates an example of various channelswithin a DL subframe of a frame. The physical control format indicatorchannel (PCFICH) is within symbol 0 of slot 0, and carries a controlformat indicator (CFI) that indicates whether the physical downlinkcontrol channel (PDCCH) occupies 1, 2, or 3 symbols (FIG. 2B illustratesa PDCCH that occupies 3 symbols). The PDCCH carries downlink controlinformation (DCI) within one or more control channel elements (CCEs),each CCE including nine RE groups (REGs), each REG including fourconsecutive REs in an OFDM symbol. A UE may be configured with aUE-specific enhanced PDCCH (ePDCCH) that also carries DCI. The ePDCCHmay have 2, 4, or 8 RB pairs (FIG. 2B shows two RB pairs, each subsetincluding one RB pair). The physical hybrid automatic repeat request(ARQ) (HARQ) indicator channel (PHICH) is also within symbol 0 of slot 0and carries the HARQ indicator (HI) that indicates HARQ acknowledgement(ACK)/negative ACK (NACK) feedback based on the physical uplink sharedchannel (PUSCH). The primary synchronization channel (PSCH) is withinsymbol 6 of slot 0 within subframes 0 and 5 of a frame, and carries aprimary synchronization signal (PSS) that is used by a UE to determinesubframe timing and a physical layer identity. The secondarysynchronization channel (SSCH) is within symbol 5 of slot 0 withinsubframes 0 and 5 of a frame, and carries a secondary synchronizationsignal (SSS) that is used by a UE to determine a physical layer cellidentity group number. Based on the physical layer identity and thephysical layer cell identity group number, the UE can determine aphysical cell identifier (PCI). Based on the PCI, the UE can determinethe locations of the aforementioned DL-RS. The physical broadcastchannel (PBCH) is within symbols 0, 1, 2, 3 of slot 1 of subframe 0 of aframe, and carries a master information block (MIB). The MIB provides anumber of RBs in the DL system bandwidth, a PHICH configuration, and asystem frame number (SFN). The physical downlink shared channel (PDSCH)carries user data, broadcast system information not transmitted throughthe PBCH such as system information blocks (SIBs), and paging messages.

As illustrated in FIG. 2C, some of the REs carry demodulation referencesignals (DM-RS) for channel estimation at the eNB. The UE mayadditionally transmit sounding reference signals (SRS) in the lastsymbol of a subframe. The SRS may have a comb structure, and a UE maytransmit SRS on one of the combs. The SRS may be used by an eNB forchannel quality estimation to enable frequency-dependent scheduling onthe UL. FIG. 2D illustrates an example of various channels within an ULsubframe of a frame. A physical random access channel (PRACH) may bewithin one or more subframes within a frame based on the PRACHconfiguration. The PRACH may include six consecutive RB pairs within asubframe. The PRACH allows the UE to perform initial system access andachieve UL synchronization. A physical uplink control channel (PUCCH)may be located on edges of the UL system bandwidth. The PUCCH carriesuplink control information (UCI), such as scheduling requests, a channelquality indicator (CQI), a precoding matrix indicator (PMI), a rankindicator (RI), and HARQ ACK/NACK feedback. The PUSCH carries data, andmay additionally be used to carry a buffer status report (BSR), a powerheadroom report (PHR), and/or UCI.

FIG. 3 is a block diagram of an eNB 310 in communication with a UE 350in an access network, where the eNB 310 may be an example of basestations 102 or 102′ and/or APs 150 of FIG. 1, and wherein the UE 350may be an example of UEs 104 and/or STAs 152 of FIG. 1. In an aspect,the tuning component 180 may be part of the UE 350, such as implementedwithin controller/processor 359 and/or memory 360. In the DL, IP packetsfrom the EPC 160 may be provided to a controller/processor 375. Thecontroller/processor 375 implements layer 3 and layer 2 functionality.Layer 3 includes a radio resource control (RRC) layer, and layer 2includes a packet data convergence protocol (PDCP) layer, a radio linkcontrol (RLC) layer, and a medium access control (MAC) layer. Thecontroller/processor 375 provides RRC layer functionality associatedwith broadcasting of system information (e.g., MIB, SIBs), RRCconnection control (e.g., RRC connection paging, RRC connectionestablishment, RRC connection modification, and RRC connection release),inter radio access technology (RAT) mobility, and measurementconfiguration for UE measurement reporting; PDCP layer functionalityassociated with header compression/decompression, security (ciphering,deciphering, integrity protection, integrity verification), and handoversupport functions; RLC layer functionality associated with the transferof upper layer packet data units (PDUs), error correction through ARQ,concatenation, segmentation, and reassembly of RLC service data units(SDUs), re-segmentation of RLC data PDUs, and reordering of RLC dataPDUs; and MAC layer functionality associated with mapping betweenlogical channels and transport channels, multiplexing of MAC SDUs ontotransport blocks (TBs), demultiplexing of MAC SDUs from TBs, schedulinginformation reporting, error correction through HARQ, priority handling,and logical channel prioritization.

The transmit (TX) processor 316 and the receive (RX) processor 370implement layer 1 functionality associated with various signalprocessing functions. Layer 1, which includes a physical (PHY) layer,may include error detection on the transport channels, forward errorcorrection (FEC) coding/decoding of the transport channels,interleaving, rate matching, mapping onto physical channels,modulation/demodulation of physical channels, and MIMO antennaprocessing. The TX processor 316 handles mapping to signalconstellations based on various modulation schemes (e.g., binaryphase-shift keying (BPSK), quadrature phase-shift keying (QPSK),M-phase-shift keying (M-PSK), M-quadrature amplitude modulation(M-QAM)). The coded and modulated symbols may then be split intoparallel streams. Each stream may then be mapped to an OFDM subcarrier,multiplexed with a reference signal (e.g., pilot) in the time and/orfrequency domain, and then combined together using an Inverse FastFourier Transform (IFFT) to produce a physical channel carrying a timedomain OFDM symbol stream. The OFDM stream is spatially precoded toproduce multiple spatial streams. Channel estimates from a channelestimator 374 may be used to determine the coding and modulation scheme,as well as for spatial processing. The channel estimate may be derivedfrom a reference signal and/or channel condition feedback transmitted bythe UE 350. Each spatial stream may then be provided to a differentantenna 320 via a separate transmitter 318TX. Each transmitter 318TX maymodulate an RF carrier with a respective spatial stream fortransmission.

At the UE 350, each receiver 354RX receives a signal through itsrespective antenna 352. Each receiver 354RX recovers informationmodulated onto an RF carrier and provides the information to the receive(RX) processor 356. The TX processor 368 and the RX processor 356implement layer 1 functionality associated with various signalprocessing functions. The RX processor 356 may perform spatialprocessing on the information to recover any spatial streams destinedfor the UE 350. If multiple spatial streams are destined for the UE 350,they may be combined by the RX processor 356 into a single OFDM symbolstream. The RX processor 356 then converts the OFDM symbol stream fromthe time-domain to the frequency domain using a Fast Fourier Transform(FFT). The frequency domain signal comprises a separate OFDM symbolstream for each subcarrier of the OFDM signal. The symbols on eachsubcarrier, and the reference signal, are recovered and demodulated bydetermining the most likely signal constellation points transmitted bythe eNB 310. These soft decisions may be based on channel estimatescomputed by a channel estimator 358. The soft decisions are then decodedand deinterleaved to recover the data and control signals that wereoriginally transmitted by the eNB 310 on the physical channel. The dataand control signals are then provided to the controller/processor 359,which implements layer 3 and layer 2 functionality.

The controller/processor 359 can be associated with a memory 360 thatstores program codes and data. The memory 360 may be referred to as acomputer-readable medium. In the UL, the controller/processor 359provides demultiplexing between transport and logical channels, packetreassembly, deciphering, header decompression, and control signalprocessing to recover IP packets from the EPC 160. Thecontroller/processor 359 is also responsible for error detection usingan ACK and/or NACK protocol to support HARQ operations.

Similar to the functionality described in connection with the DLtransmission by the eNB 310, the controller/processor 359 provides RRClayer functionality associated with system information (e.g., MIB, SIBs)acquisition, RRC connections, and measurement reporting; PDCP layerfunctionality associated with header compression/decompression, andsecurity (ciphering, deciphering, integrity protection, integrityverification); RLC layer functionality associated with the transfer ofupper layer PDUs, error correction through ARQ, concatenation,segmentation, and reassembly of RLC SDUs, re-segmentation of RLC dataPDUs, and reordering of RLC data PDUs; and MAC layer functionalityassociated with mapping between logical channels and transport channels,multiplexing of MAC SDUs onto TBs, demultiplexing of MAC SDUs from TBs,scheduling information reporting, error correction through HARQ,priority handling, and logical channel prioritization.

Channel estimates derived by the channel estimator 358 from a referencesignal or feedback transmitted by the eNB 310 may be used by the TXprocessor 368 to select the appropriate coding and modulation schemes,and to facilitate spatial processing. The spatial streams generated bythe TX processor 368 may be provided to different antenna 352 viaseparate transmitters 354TX. Each transmitter 354TX may modulate an RFcarrier with a respective spatial stream for transmission.

The UL transmission is processed at the eNB 310 in a manner similar tothat described in connection with the receiver function at the UE 350.Each receiver 318RX receives a signal through its respective antenna320. Each receiver 318RX recovers information modulated onto an RFcarrier and provides the information to a RX processor 370.

The controller/processor 375 can be associated with a memory 376 thatstores program codes and data. The memory 376 may be referred to as acomputer-readable medium. In the UL, the controller/processor 375provides demultiplexing between transport and logical channels, packetreassembly, deciphering, header decompression, control signal processingto recover IP packets from the UE 350. IP packets from thecontroller/processor 375 may be provided to the EPC 160. Thecontroller/processor 375 is also responsible for error detection usingan ACK and/or NACK protocol to support HARQ operations.

Referring to FIG. 4, a wireless communications system 400, which may besimilar to wireless communications system 100 of FIG. 1, may includeadditional system components in one exemplary implementation for tuninga modem 402 of a UE 104 using the tuning component 180 in conjunctionwith the USS 182.

In particular, wireless communications system 400 includes a UE 104having a modem 402, a tuning component 180 having a set of modemalgorithms 404 and a set of hypotheses 406. The modem 402 is configuredto connect the UE 104 to a wireless network, e.g., IP services 176, viaan access point, e.g., a base station 102. The modem 402 is configuredto send and receive data, e.g., voice and/or data. The tuning component180 is configured to tune the modem 402 by employing a selectedhypothesis from the set of hypotheses 406 that are stored on the UE 104.The selected hypothesis can be a selected unit sourced hypothesis or aselected crowd sourced hypothesis. The selected unit sourced hypothesiscan be employed when the UE 104 is connected to a wireless network via anetwork access point that the UE 104 has used in a given time period. Auser of UE 104 can frequently connect to the same network access pointsbased on their routine for a given time period, e.g., weekly or monthly.For example, a user of UE 104 can connect to the same network accesspoint when the user stops for morning coffee and can connect to adifferent network access point when the user arrives at work. This canbe referred to as an UE frequented route. The selected crowd sourcedhypothesis can be employed when the UE 104 is connected to the wirelessnetwork via a network access point that the UE 104 has not used in thegiven time period. For example, the selected crowd sourced hypothesiscan be employed if a user takes a trip outside of his or her UEfrequented route. The UE 104 can use the selected hypothesis havingassociated weights to execute a corresponding modem function, such asswitch from WiFi to LTE. In response to a triggering point the tuningcomponent 180 can send information associated with the features for theexecuted modem function to the USS 182. For example, the UE 104 sendsfeature values (x₁, x₂, . . . , x_(n)) and state information associatedwith the corresponding modem function, if available, in response to atriggering point or event being met. The feature values and stateinformation is explained below. The state information is an expectedoutcome. For example, state information can be switch from WiFi to LTEor stay on WiFi.

The USS 182 receives and can archive the received information/data. TheUSS 182 can run one or more learning algorithms 410 based on theinformation received from the tuning component 180. As explained in moredetail below, the USS 182 can use the received information from the UE104 and/or use crowd sourced information to select a unit sourcedhypothesis and associated weights and a crowd sourced hypothesis andassociated weights. The crowd sourced data can be obtained from a crowdsourcing server 184. The crowd sourcing server 184 can includeinformation learned from multiple UEs. As a result, the USS 182 can tunethe modem, post deployment, based on information from the UE and/orcrowd sourced information.

The UE 104 can include a plurality of modem algorithms 404 with eachexecuting a specific modem function. Each modem algorithm 404 caninclude a plurality of features (x_(i)) associated with a modemfunction. For example, the features can be Reference Signal ReceivedQuality (RSRQ), Hybrid Automatic Repeat request (HARQ) 1^(st) packeterror, Residual HARQ Block Error Rate (BLER), Real-Time TransportProtocol (RTP) PER, RTP end to end (e2e) delay, etc. The features (X)can be combined. For example, a combination of features can be {x₁, x₂,x₃, . . . , x_(n)}. Each feature x_(i) can be a linear representation, apolynomial representation (x_(i) ^(k)) and/or a product of multiplefeatures (x_(i)*x_(j)). The modem algorithm 404 can include linear andquadratic forms for all of the features. Each modem algorithm 404 caninclude one or more expected results (y_(i)) for the executed modemfunction. The expected results can be a set of output conditions thatare considered to be met. The expected results can be referred to asstate information which is the state when a modem function is executed.The state information can be optional.

For example, a modem algorithm 404 can be for LTE-WiFi handover (HO) forVoice over LTE (VOLTE). Table I shows the features (x_(i)) and expectedresults (Y_(i)).

TABLE I LTE-WiFi handover (HO) for Voice over LTE (VoLTE). X x₁ WiFi RSSx₂ MAC retry count x₃ MAC PER x₄ MAC Uplink quality metric x₅ MACDownlink quality metric x₆ Adaptive dejitter buffer depth x₇ 95^(th)Percentile DL Relative jitter x₈ DL Packet loss x₉ Average UL Relativejitter x₁₀ UL Packet loss x₁₁ LTE RSRP x₁₂ LTE RSRQ x₁₃ LTE SINR X₁₄WiFi Access Point BSSID, corresponding cellular cluster x₁₅ (WiFi RSSI)²x₁₆ (MAC retry count)² x₁₇ (MAC PER)² . . . x_(n) Y Y₁ HO to LTE Y₂Remain on WiFi Y₃ CS repoint

In another example, a modem algorithm 404 can be for Modem VoltageSetting.

Table II shows the features (x_(i)) and expected result (Y_(i)).

TABLE II Modem Voltage Setting X x₁ PDSCH PER x₂ RLC error (HARQresidual error) x₃ RSRP/RSRQ . . . x_(n). Y Y₁ High Y₂ Low

Tables I and II list the features x_(i) in linear and quadratic form.Table I shows three expected outcomes (Y) for LTE-WiFi HO for VOLTE:handover to LTE, remain on WiFi and association remains on WiFi butpoint to circuit switch (CS) for calling purposes. Table II shows twoexpected outcomes (Y) for Modem Voltage Settings: high and low.

When executing a modem function, the modem 402 and/or tuning component180 employs a selected hypothesis having associated weights for eachfeature of the modem algorithm 404. The selected hypothesis is selectedfrom the set of hypothesis 406 stored on the UE 104, e.g., in the memoryof the UE 104. Each hypothesis (h_(k)(x)) includes a weightedcombination of features x_(i). The UE 104 is provisioned with multiplepossible hypothesis h_(k)(x) for k=1 to m, where m may be a value up toany number. A hypothesis can be a linear regression, logistic regressionor a support vector machine (SVM) with associated weights. For example,a linear regression or SVM hypothesis can be represented byh_(k)(x)=θ₀+θ₁x₁+θ₂x₂+ . . . θ_(n)x_(n), where θ_(j) are the weightsapplied to each feature. A logistic regression can be represented byh_(k)(x)=1/(1+exp^(−θ0+θ1×1+θ2×2+ . . . θn×n)), where θ_(j) are theweights applied to each feature. A hypothesis can include neural networkinformation associated with the hypothesis for handling hidden layersand applying different techniques for the representations for each layeror regression.

There are two types of hypotheses 406: unit sourced hypotheses and crowdsourced hypotheses. A selected unit sourced hypothesis is used when theUE 104 accesses a frequented access node and a selected crowd sourcedhypothesis is used when the UE 104 accesses a non-frequented accessnode. A frequented access node is an access node that the UE 104 haspreviously accessed within a given time period. For example, afrequented access node is an access node that the UE 104 accesses in aUE frequented route, such as when the UE user goes to work. Anon-frequented access node is an access node the UE 104 has notpreviously accessed within a given time period. For example, anon-frequented access node is an access node that a UE 104 accesses whengoing outside the frequented route, such as when the UE user goes ontravel.

In an aspect, other criteria can be used to arbitrate across the twoselected hypotheses. For example, if the UE 104 has a dual modem, theunit sourced hypothesis can be employed on a first modem and the crowdsourced hypothesis can be employed on a second modem. Alternatively, themodem 402 can switch off between the two selected hypothesis to gatherinformation to determine which selected hypothesis performs better.

At specific trigger points, the UE 104 can send information to the USS182. The information can include the metrics for the features associatedwith the executed modem algorithm. The metrics can be real values and/orcan be Boolean values. For example, if a metric is for a voltage, theactual voltage can be sent or a Boolean value indicating whether thevoltage is above a threshold or below a threshold can be sent. Thethreshold can be a predetermined value. If state information isavailable, the state information can be sent as well. The stateinformation can be sent when the UE 104 is able to determine theexpected outcome for a given input X. When state information is notprovided to the USS 182, the USS 182 treats this information asunclassified and employs unsupervised learning, which uses clustering ofthe data to classify the data. This mechanism can be combined with crowdsourced information to determine consistent versus anomalous behaviors.The anomalous behavior (e.g., anything outside the identified clusters)can be used as triggering functions as well.

For example, if the modem function is LTE-WiFi HO for VoLTE, the UE 104would send the metrics for the features (x_(i)) listed in Table 1. Inaddition, if one of the expected outcomes was reached, e.g., HO to LTE,remain on WiFi or CS repoint, the state information can be sent to theUSS 182. The trigger points can be the expected outcomes and/or othertrigger points. For example, if a call is dropped, the dropping of thecall can be a trigger point. For some modem functions, there may not bean expected outcome.

The information that the UE 104 sends to the USS 182 can include theselected hypothesis, the selected unit sourced hypothesis or theselected crowd sourced hypothesis. The information can include theweights associated with the selected hypothesis. Since the USS 182provided the selected hypothesis, the USS 182 can identify the weightsassociated with the selected hypothesis.

Also, the information that the UE 104 sends to the USS 182 can include adevice identifier of the UE 104. In an aspect, the device identifier maybe a random identifier so that the provided information cannot bedirectly traced to the UE 104. The random identifier can be generated bythe UE 104 or can be selected from a group of device identifiers thatare stored on the UE 104. Additionally, the information that the UE 104sends can include a time identifier which indicates a time period thatthe USS 182 can store or archive the information from the UE 104. TheUSS 182 can use the received information for crowd sourcing.

Additionally, the information that the UE 104 sends to the USS 182 caninclude a modem algorithm identifier. The modem algorithm identifier canbe used to identify the version of the set of hypotheses 406 that arestored on the UE 104. Further, the information that the UE 104 sends caninclude information requesting specific approaches amongst the set ofhypotheses 406 stored on the UE 104. For example, if the modem 402 haslimited processing capability, the UE 104 may want to only use linearregression hypotheses as opposed to a more processor-intensivehypothesis. For example, the UE 104 can send identification of a subsetof hypotheses, e.g., linear regression hypotheses, that the UE 104 canuse. The identification of the subset of hypotheses can limit thehypotheses that the USS 182 can select. The subset of hypothesescontains less hypotheses than the set of hypotheses 406.

The USS 182 receives the information from the UE 104. The USS 182 storesor archives the received information. The USS 182 runs one or morelearning algorithms 410 to select hypotheses and weights for the UE 104.The one or more learning algorithms 410 can be unsupervised learningalgorithms. The unsupervised learning algorithms can be knownalgorithms, such as, liner regression, logistic regression, SVM, etc.For metrics and state information that is received from the UE 104employing a selected unit sourced hypothesis, the USS 182 runs one ormore learning algorithms 410 to cluster the information, e.g., knownk-means unsupervised learning algorithm, and select a unit sourcedhypothesis and associated weights for the corresponding modem function.The one or more learning algorithms 410 can use information from the UEthat performed the same modem function using the same selected unitsourced hypothesis and/or one or more different selected unit sourcedhypotheses.

If the received information includes metrics, but not state information,from a UE 104 that employed a selected unit sourced hypothesis, the USS182 can run the one or more learning algorithms 410 using crowd sourcedinformation to cluster the information and select a unit sourcedhypothesis and associated weights. The crowd sourced information can beobtained from a crowd sourcing server 184. The USS 182 can provide theoutput (e.g., state information) that is obtained from the crowd sourcedinformation to the UE 104 which can serve as a trigger point. The crowdsourced information can be used to override information received fromthe UE 104.

If the received information from the UE 104 employing a selected crowdsourced hypothesis, the USS 182 can run the one or more learningalgorithms 410 using crowd sourced information to cluster theinformation and select a crowd sourced hypothesis and associatedweights. The crowd sourced information can be obtained from a crowdsourcing server 184.

The clustering can indicate good results and bad results. For example, agood result is closely correlated to a preferred cluster and a badresult is an anomaly that is not closely correlated to a preferredcluster. Classification can be applied on the clusters to employ anomalydetection based on falling out of the identified clusters. To prevent orminimize anomalies, the USS 182 can use an anomaly as a trigger point.For example, the USS 182 can send the classification information, e.g.,y, to the UE 104. The trigger point (e.g., y) can be sent as stateinformation which can supplement or replace state information on the UE104. The sent state information can be used as a trigger point when theassociated modem function is executed by the modem 402. In oneimplementation, for example, a benefit can be to allow for the modemalgorithms 404 to be deployed, particularly where the relevantparameters and the relative association in terms of determining triggerpoints is not well understood prior to deployment of the UE 104. Thus,the parameters for the modem algorithms 404 can be tuned postdeployment. This also allows for more parameters to be included makingthe hypotheses selection itself dynamic enabling a flexible algorithmdefinition.

After selecting a user sourced hypothesis and associated weights andselecting a crowd sourced hypothesis and associated weights, the USS 182can send the selected hypotheses to the UE 104. The UE 104 can receivethe selected hypotheses and selected weights and can apply them formeeting specific trigger conditions on the device. As a result, themodem 402 of the UE 104 can be tuned using unit sourcing so that thealgorithms can be fine-tuned to cater to the experiences specific to UE104. By fine-tuning the features or parameters of the modem functions,the behavior of the modem 402 is customized rather than usinggeneralized parameters that are applicable to all UEs in all markets.

The USS 182 can use one or more of crowd sourcing, cloud computing,artificial neural networks and neural network processor unit (NPU) toprocess the received information to select the hypotheses and associatedweights. Crowd sourcing in the practice of obtaining needed services,ideas, or content by soliciting contributions from a large group ofpeople and especially from the online community rather than fromtraditional employees or suppliers. Crowd sourcing is typically employedin the cell phone industry to collect data from different devices in themarket to detect patterns of failures and identifying optimizations bothin the network and devices. Cloud computing is the practice of using anetwork of remote servers hosted on the Internet to store manage, andprocess data, rather than a local server or a personal computer. Inmachine learning and cognitive science, artificial neural networks (ANN)are a family of models inspired by biological neural networks (thecentral nervous systems of animals, in particular the brain (which areused to estimate or approximate functions that can depend on a largenumber of inputs and are generally unknown with one or more hiddenlayers for training purposes. A NPU, typically consists of a graphicsprocessing unit (GPU), central processing unit (CPU) and a digitalsignal processor (DSP) used for neural network computing on cellulardevices. Neural networks can be applied to enable the hypotheses ifrelevant. If hidden layers are required with neural network processing,the layers and associated weights are provided to the UE 104. The UE 104can use this to apply a hypothesis for each layer.

Referring to FIGS. 5 and 6, an example aspect of a method 500 ofwireless communication performed by a UE 104 to tune a modem 402 of theUE 104 and a hardware implementation for performing the method 500. Forexample, method 500 relates to the above-discussed implementations, andmay be performed by the UE 104, such as the modem 402 and/or the tuningcomponent 180.

At block 502, method 500 includes accessing a network via a networkaccess node. For example, a modem 402 accesses a network, e.g. IPservices 176, via a network access node, e.g., via a base station 102.

At block 504, method 500 includes determining whether the network accessnode is a frequented node or a non-frequented node. For example, a nodedeterminer component 604 determines whether the network access node is afrequented node or a non-frequented node. A frequented node is a networkaccess node that the UE 104 has previously accessed in a given timeperiod and a non-frequented node is a network access node that the UE104 has not previously accessed in the given time period. The given timeperiod can be a pre-determined value such as, one week, two weeks or amonth.

At block 506, method 500 includes executing a modem function using acorresponding selected hypothesis having associated weights for eachfeature associated with the modem function. For example, the modem 402,or the modem 402 in conjunction with the tuning component 180, executesa modem function using a selected hypothesis having associated weightsfor each feature associated with the modem function. The selectedhypothesis is a unit sourced hypothesis if the network access node isdetermined to be a frequented node. The selected hypothesis is a crowdsourced hypothesis if the network node is determined to be anon-frequented access node. The selected hypothesis is one of a set ofhypotheses 406 stored on the UE 104 with each hypothesis correspondingto a modem function and including a plurality of features and stateinformation. Initially, when the UE 104 is deployed, the tuningcomponent 180 can use a default hypothesis as the selected hypothesis.For example, the original equipment manufacturer can set the initialweights prior to deployment of the modem 402/UE 104.

At block 508, method 500 includes sending information, to the USS 182,the information comprising a device identifier, the modem function, theselected hypothesis and associated weights, metrics for each feature andstate information, if available, in response to a trigger point beingmet when executing the modem function. For example, a sending component606 sends, to the USS 182, a device identifier, the modem function, theselected hypothesis and associated weights, metrics for each feature andstate information, if available, in response to a trigger point beingmet when executing the modem function. In one implementation, thesending component 606 sends a modem algorithm identifier which indicatesa version of the set of hypotheses 406 stored on the UE 104. In oneimplementation, the sending component 606 sends a subset identifier tothe USS 182 identifying a subset of hypotheses that the UE 104 can use.

At block 510, method 500 includes receiving, from the USS 182, aselection of at least one of a unit sourced hypothesis or a crowdsourced hypothesis for a corresponding modem function, with the selectedhypothesis including weights for each feature. For example, a receivingcomponent 608 receives, from the USS 182, a selection of at least one ofa unit sourced hypothesis or a crowd sourced hypothesis for acorresponding modem function, with the selected hypothesis includingweights for each feature. For example, the receiving component 608 canreceive a selected unit sourced hypothesis, a selected crowd sourcedhypothesis or both of a selected unit sourced hypothesis and a selectedcrowd sourced hypothesis. The selected unit sourced hypothesis can bebased on information that the UE 104 sent to the USS 182, with theinformation being run through one or more learning algorithms 410 andthe selected crowd sourced hypothesis based on crowd sourced data thatwas run through one or more learning algorithms 410.

FIG. 6 is a diagram 600 illustrating an example of a hardwareimplementation for an apparatus 600, e.g., UE 104, employing aprocessing system 602. The processing system 602 may be implemented witha bus architecture, represented generally by the bus 610. The bus 610may include any number of interconnecting buses and bridges depending onthe specific application of the processing system 602 and the overalldesign constraints. The bus 610 links together various circuitsincluding one or more processors and/or hardware components, representedby the processor 612, the components 180, 402, 602, 604, 606, 608 and610 and the computer-readable medium/memory 614. The bus 610 may alsolink various other circuits such as timing sources, peripherals, voltageregulators, and power management circuits, which are well known in theart, and therefore, will not be described any further.

The processing system 602 may be coupled to a transceiver 616. Thetransceiver 616 is coupled to one or more antennas 618. The transceiver616 provides a means for communicating with various other apparatus,e.g., the USS 182 over a transmission medium. The transceiver 616receives a signal from the one or more antennas 618, extractsinformation from the received signal, and provides the extractedinformation to the processing system 602, specifically the receptioncomponent 620 of modem 402. In addition, the transceiver 616 receivesinformation from the processing system 602, specifically thetransmission component 622, and based on the received information,generates a signal to be applied to the one or more antennas 618. Theprocessing system 602 includes a processor 612 coupled to acomputer-readable medium/memory 614. The processor 612 is responsiblefor general processing, including the execution of software stored onthe computer-readable medium/memory 614. The software, when executed bythe processor 612, causes the processing system 602 to perform thevarious functions described supra for any particular apparatus. Thecomputer-readable medium/memory 614 may also be used for storing datathat is manipulated by the processor 612 when executing software. Theprocessing system 602 further includes at least one of the components180, 402, 602, 604, 606, 608 and 610. The components may be softwarecomponents running in the processor 612, resident/stored in the computerreadable medium/memory 614, one or more hardware components coupled tothe processor 612, or some combination thereof.

The apparatus may include additional components that perform each of theactions described with respect to the aforementioned flowchart of FIG. 5and/or the aspects of FIGS. 4-8. As such, each action described withreference to the aforementioned flowchart of FIG. 5 and/or the aspectsof FIGS. 4-8 may be performed by a component and the apparatus mayinclude one or more of those components. The components may be one ormore hardware components specifically configured to carry out the statedprocesses/algorithm, implemented by a processor configured to performthe stated processes/algorithm, stored within a computer-readable mediumfor implementation by a processor, or some combination thereof.

Referring to FIGS. 7A, 7B and 8, an example aspect of a method 700 ofwireless communication performed by the USS 182 to tune the modem 402 ofthe UE 104 and a hardware implementation for performing the method 700.For example, method 700 relates to the above-discussed implementations,and may be performed by the USS 182.

At block 702, method 700 includes receiving information from a UE. Forexample, accessing a network via a network access node. For example, theUSS 182 receives the information from the UE 104 via a receptioncomponent 806. The information can include a device identifier, a modemfunction executed by the UE 104, a selected hypothesis and associatedweights for the corresponding modem functions, metrics for each featureof the corresponding modem function and state information, if the stateinformation is available. The information is sent in response to atrigger point associated with the modem function being met when themodem function is executed.

At block 704, method 700 includes storing the received information. Forexample, the USS 182 stores the received information on a UE basis usingthe device identifier. The information can be stored locally orremotely, e.g., on one or more servers communicatively coupled to theUSS 182.

At block 706, method 700 includes running one or more learningalgorithms based on the stored information to cluster the informationand select a unit sources hypothesis and associated weights for thecorresponding modem function when state information is provided by theUE. For example, the USS 182 runs the one or more learning algorithms410 based on the stored information to cluster the information andselect a unit sourced hypothesis and associated weights for thecorrespond modem function.

At block 708, method 700 includes running one or more learningalgorithms based on the crowd sourced information to cluster theinformation and select a unit sourced hypothesis and associated weightsfor the corresponding modem function when state information was notprovided by the UE. For example, the USS 182 runs the one or morelearning algorithms 410 based on crowd sourced information to clusterthe information and select a unit sourced hypothesis and associatedweights for the correspond modem function. The crowd sourced informationcan be obtained from one or more crowd sourcing servers 184.

At block 710, method 700 includes selecting the unit sourced hypothesisand associated weights based on the one or more learning algorithms. Forexample, a hypothesis selecting component 804 can select the unitsourced hypothesis and associated weights based on the one or morelearning algorithms 410. The received information can include a modemalgorithm identifier which can identify the version of the set ofhypotheses 406 stored on the UE 104. The received information caninclude a request for specific approaches amongst the set of hypotheses406 stored on the UE 104. For example, if the modem 402 resides in a UE104 that has limited processing capability, the UE 104 may want to onlyuse linear regression hypotheses. The UE 104 can send a subset ofhypotheses, e.g., linear regression hypotheses, that the UE 104 prefersto employ. Thus the received information can limit the hypotheses thatthe USS 182 can select.

At block 712, method 700 includes running one or more learningalgorithms based on the crowd sourced information to cluster theinformation and select a crowd sourced hypothesis and associated weightsfor the corresponding modem function when the state information wasprovided by the UE. For example, the USS 182 runs the one or morelearning algorithms 410 to cluster the information and select a crowdsourced hypothesis and associated weights for the correspond modemfunction when the state information was provided by the UE 104. Thecrowd sourced information can be obtained from one or more crowdsourcing servers 184.

At block 714, method 700 includes selecting the crowd sourced hypothesisand associated weights based on the one or more learning algorithms. Forexample, a hypothesis selecting component 804 can select the crowdsourced hypothesis and associated weights based on the one or morelearning algorithms.

At block 716, method 700 includes sending at least one of the selectedunit sourced hypothesis and associated weights or selected crowd sourcedhypothesis and associated weights for the corresponding modem function.For example, the USS 182 sends at least one of the selected hypothesesand associated weights using the transmission component 808. In anotherexample, the USS 182 sends the at least one selected unit sourcehypothesis and associated weights or the selected crowd sourcehypothesis and associated weights using the transmission component 808.For example, the transmission component 808 can send a selected unitsourced hypothesis, a selected crowd sourced hypothesis or both of aselected unit sourced hypothesis and a selected crowd sourcedhypothesis.

At block 718, method 700 can optionally include sending stateinformation associated with the selected unit sourced hypothesis whenthe USS 182 ran one or more learning algorithms 410 based on crowdsource information when the state information was not provided by the UE104. For example, the USS 182 sends the state information using thetransmission component 808. The state information can be used tosupplement or replace state information on the UE 104. The sent stateinformation can be used as a trigger point when the associated modemfunction is executed by the modem 402.

At block 720, method 700 can optionally include sending at least one ofstate information associated with the selected unit sourced hypothesisor state information associated with the selected crowd sourcedhypothesis. For example, the USS 182 sends the state information usingthe transmission component 808. The state information can be used tosupplement or replace state information on the UE 104. The sent stateinformation can be used as a trigger point when the associated modemfunction is executed by the modem 402.

FIG. 8 is a diagram 800 illustrating an example of a hardwareimplementation for an apparatus 800, e.g., a USS 182, employing aprocessing system 802. The processing system 802 may be implemented witha bus architecture, represented generally by the bus 810. The bus 810may include any number of interconnecting buses and bridges depending onthe specific application of the processing system 802 and the overalldesign constraints. The bus 810 links together various circuitsincluding one or more processors and/or hardware components, representedby the processor 812, the components 410, 804, 806 and 808 and thecomputer-readable medium/memory 814. The bus 810 may also link variousother circuits such as timing sources, peripherals, voltage regulators,and power management circuits, which are well known in the art, andtherefore, will not be described any further.

The processing system 802 may be coupled to a transceiver 816. Thetransceiver 816 is coupled to one or more antennas 818. The transceiver816 provides a means for communicating with various other apparatus overa transmission medium. The transceiver 816 receives a signal from theone or more antennas 818, extracts information from the received signal,and provides the extracted information to the processing system 802,specifically the reception component 806. In addition, the transceiver816 receives information from the processing system 802, specificallythe transmission component 808, and based on the received information,generates a signal to be applied to the one or more antennas 818. Theprocessing system 802 includes a processor 812 coupled to acomputer-readable medium/memory 814. The processor 812 is responsiblefor general processing, including the execution of software stored onthe computer-readable medium/memory 814. The software, when executed bythe processor 812, causes the processing system 802 to perform thevarious functions described supra for any particular apparatus. Thecomputer-readable medium/memory 814 may also be used for storing datathat is manipulated by the processor 812 when executing software. Theprocessing system 802 further includes at least one of the components410, 804, 806 and 808. The components may be software components runningin the processor 812, resident/stored in the computer readablemedium/memory 814, one or more hardware components coupled to theprocessor 812, or some combination thereof.

The apparatus may include additional components that perform each of theactions described with respect to the aforementioned flowchart of FIGS.7A and 7B and/or the aspects of FIGS. 4-8. As such, each actiondescribed with reference to the aforementioned flowchart of FIGS. 7A and7B and/or the aspects of FIGS. 4-8 may be performed by a component andthe apparatus may include one or more of those components. Thecomponents may be one or more hardware components specificallyconfigured to carry out the stated processes/algorithm, implemented by aprocessor configured to perform the stated processes/algorithm, storedwithin a computer-readable medium for implementation by a processor, orsome combination thereof.

As discussed earlier, neural networks can be used to assist a hypothesisto handle hidden layers and apply different techniques for therepresentations for each layer or regression. Neural networks allows fora computationally less intensive approach to implement a non-linearrealization. The real time computations can be triggered based on thespecific metrics changing beyond a specific delta value and theoptimization of reducing or minimizing the computations can be tunedaccordingly. The delta can be tuned on a per metric basis and caninclude a range of operations for that metric. For example, for aLTE-WiFi handover modem hypothesis, the RSSI delta value at a higherlevel, such as −50 dBM, will be different from the RSSI delta value usedwhen the RSSI is at −85 dBm. The training of the neural networks can bedone in the USS 182 and provide a means to ensure consistency. Forexample, one mechanism can apply a threshold level to determine the RTPerror rate. In such a scenario, the RTP error rate can be a “1” when theRTP error rate is above a threshold level (e.g., 3%) and can be a “0”when the RTP error rate is below a threshold level (e.g., 3%). Inanother example, a second mechanism can apply an objective Means OpinionScore (MOS) prediction software to threshold the RTP error rate. In sucha scenario, the MOS prediction software can generate a threshold of 3.2%with a “1” being outputted for an RTP error rate above the 3.2%threshold and a “0” being outputted for an TRP error rate below the 3.2%threshold.

FIG. 9 is a diagram 900 illustrating a Neural Network based approach forLTE-WiFi handover management. As shown, the input layer contains metricsfor features for LTE-WiFi handover. The metrics can be paired todetermine correlated behaviors, For example, as shown, the parametersfrom the UE 104 are shown as an input layer with the weights (θ₍₁₎) as afirst layer, the correlated parameters as hidden layer 1, the weights(θ₍₂₎) as a second layer, the combination of correlated parameters ashidden layer 2, the (θ₍₃₎) as a third layer and an output layer h_(k)(x)having three state values: Y1: handover to LTE, Y2: remain on WiFi andY3: circuit switch repoint. As shown, the WiFi RSSI and MAC retry countare correlated, WiFi RSSI and MAC PER are correlated, WiFi RSSI andadaptive dejitter buffer depth are correlated, adaptive dejitter bufferdepth and 95th percentile DL relative jitter are correlated, adaptivedejitter buffer depth and LTE RSRP are correlated, and LTE RSRP and LTERSRQ are correlated. The pairwise group in the hidden layer 1 allow forcorrelation across any two metrics to be captured. The hidden layer 2allows for MAC layer specific metrics base computation to be executedand the same computations can be made available to other application. Asshown, the pairwise combinations can be correlated by combining WiFimetrics and by combining media, WiFi and LTE metrics. Based on thecombinations, one or more state information can be determined asexpected outputs, e.g., ground truth, which allow for learning thebehavior for a given device and also lean on patterns seen through thecrowd sourced data.

It is understood that the specific order or hierarchy of blocks in theprocesses/flowcharts disclosed is an illustration of exemplaryapproaches. Based upon design preferences, it is understood that thespecific order or hierarchy of blocks in the processes/flowcharts may berearranged. Further, some blocks may be combined or omitted. Theaccompanying method claims present elements of the various blocks in asample order, and are not meant to be limited to the specific order orhierarchy presented.

The previous description is provided to enable any person skilled in theart to practice the various aspects described herein. Variousmodifications to these aspects will be readily apparent to those skilledin the art, and the generic principles defined herein may be applied toother aspects. Thus, the claims are not intended to be limited to theaspects shown herein, but is to be accorded the full scope consistentwith the language claims, wherein reference to an element in thesingular is not intended to mean “one and only one” unless specificallyso stated, but rather “one or more.” The word “exemplary” is used hereinto mean “serving as an example, instance, or illustration.” Any aspectdescribed herein as “exemplary” is not necessarily to be construed aspreferred or advantageous over other aspects. Unless specifically statedotherwise, the term “some” refers to one or more. Combinations such as“at least one of A, B, or C,” “one or more of A, B, or C,” “at least oneof A, B, and C,” “one or more of A, B, and C,” and “A, B, C, or anycombination thereof” include any combination of A, B, and/or C, and mayinclude multiples of A, multiples of B, or multiples of C. Specifically,combinations such as “at least one of A, B, or C,” “one or more of A, B,or C,” “at least one of A, B, and C,” “one or more of A, B, and C,” and“A, B, C, or any combination thereof” may be A only, B only, C only, Aand B, A and C, B and C, or A and B and C, where any such combinationsmay contain one or more member or members of A, B, or C. All structuraland functional equivalents to the elements of the various aspectsdescribed throughout this disclosure that are known or later come to beknown to those of ordinary skill in the art are expressly incorporatedherein by reference and are intended to be encompassed by the claims.Moreover, nothing disclosed herein is intended to be dedicated to thepublic regardless of whether such disclosure is explicitly recited inthe claims. The words “module,” “mechanism,” “element,” “device,” andthe like may not be a substitute for the word “means.” As such, no claimelement is to be construed as a means plus function unless the elementis expressly recited using the phrase “means for.”

What is claimed is:
 1. A method for wireless communications, comprising:accessing a wireless network via a network access node; determiningwhether the network access node is a frequented node or a non-frequentednode based on an identifier for the network access node; executing amodem function using a corresponding selected hypothesis havingassociated weights for each feature associated with the modem function,the selected hypothesis being a unit sourced hypothesis if the networkaccess node is determined to be a frequented node and the selectedhypothesis being a crowd sourced hypothesis if the network access nodeis determined to be a non-frequented node, wherein the selectedhypothesis is one of a set of hypotheses stored on the UE with eachhypothesis corresponding to a modem function and including a pluralityof features, state information and at least one trigger point; andsending information, to a server, the information comprising a deviceidentifier identifying the UE, the modem function, the selectedhypothesis and associated weights, metrics for each feature and stateinformation, if the state information is available, in response to atrigger point being met when executing the modem function.
 2. The methodof claim 1, wherein each hypothesis is one of linear regression, supportvector machine and logistic regression.
 3. The method of claim 2,wherein the selected hypothesis includes associated neural networksinformation for applying the hypothesis.
 4. The method of claim 1,further comprising sending, to the server, a modem algorithm identifieridentifying a version of the set of hypotheses stored on the UE.
 5. Themethod of claim 1, further comprising sending, to the server, a subsetof hypotheses that the UE can use wherein the subset contains lesshypotheses than the set of hypotheses stored on the UE.
 6. The method ofclaim 1, further comprising receiving, from the server, a selected unitsourced hypothesis and a selected crowd sourced hypothesis, with eachselection corresponding to a modem function and including weights foreach feature associated with the modem function, wherein the selectedunit sourced hypothesis is based on information the UE sent to theserver and the information being run through one or more learningalgorithms and the selected crowd sourced hypothesis based on crowdsourced data.
 7. The method of claim 6 wherein the one or more learningalgorithms are unsupervised learning algorithms.
 8. A method forwireless communications, comprising: receiving information, at a serverfrom a user equipment (UE), the information comprising a deviceidentifier, a modem function executed by the UE, a selected hypothesisfor the corresponding modem function, metrics for each featureassociated with the corresponding modem function and state information,if the state information is available, in response to a trigger pointassociated with the modem function being met when executing the modemfunction; storing, by the server, the received information; running, bythe server, one or more learning algorithms based on the storedinformation to cluster the information and to select a unit sourcedhypothesis and associated weights for the corresponding modem functionwhen the UE provided the state information; running, by the server, oneor more learning algorithms based on crowd sourced information fromother UEs to cluster the information and to select a unit sourcedhypothesis and associated weights for the corresponding modem functionwhen the state information was not provided by the UE; selecting, by theserver, the unit sourced hypothesis and associated weights based on theone or more learning algorithms; running, by the server, one or morelearning algorithms based on crowd sourced information from other UEs tocluster the information and to select a crowd sourced hypothesis andassociated weights for the corresponding modem function when the UEprovided the state information; selecting, by the server, the crowdsourced hypothesis and associated weights based on the one or morelearning algorithms; and sending, by the server to the UE, at least oneof the selected unit sourced hypothesis and associated weights or theselected crowd sourced hypothesis and associated weights for thecorresponding modem function.
 9. The method of claim 8, wherein thereceived information further comprises weights associated with theselected hypothesis for the corresponding modem function.
 10. The methodof claim 8, further comprising, receiving by the server from the UE, amodem algorithm identifier indicating a version of a set of hypothesesstored on the UE and the selected unit sourced hypothesis and theselected unit sourced hypothesis are selected from the set of hypothesesstored on the UE.
 11. The method of claim 8, further comprisingreceiving by the server from the UE, a subset of hypotheses that the UEcan use and the and the selected unit sourced hypothesis and theselected unit sourced hypothesis are selected from the subset ofhypotheses stored on the UE, where the subset is less than a set ofhypotheses stored on the UE.
 12. The method of claim 8, wherein eachhypothesis is one of linear regression, support vector machine andlogistic regression.
 13. The method of claim 12, further comprisingemploying neural networks and sending neural networks information forapplying a selected hypothesis.
 14. The method of claim 8, wherein theone or more learning algorithms are unsupervised learning algorithms.15. The method of claim 11, further comprising: sending, by the serverto the UE, state information associated with a selected unit sourcedhypothesis when the server ran one or more learning algorithms based oncrowd sourced information when the state information was not provided bythe UE; and sending, by the server to the UE, at least one of stateinformation associated with a selected unit sourced hypothesis and stateinformation associated with a selected crowd sourced hypothesis.
 16. Anapparatus for wireless communication, comprising: a transceiver; amemory configured to store instructions; and one or more processorscommunicatively coupled with the transceiver and the memory, wherein theone or more processors are configured to execute instructions to: accessa wireless network via a network access node; determine whether thenetwork access node is a frequented node or a non-frequented node basedon an identifier for the network access node; execute a modem functionusing a corresponding selected hypothesis having associated weights foreach feature associated with the modem function, the selected hypothesisbeing a unit sourced hypothesis if the network access node is determinedto be a frequented node and the selected hypothesis being a crowdsourced hypothesis if the network access node is determined to be anon-frequented node, wherein the selected hypothesis is one of a set ofhypotheses stored on the UE with each hypothesis corresponding to amodem function and including a plurality of features, state informationand at least one trigger point; and send information, to a server, theinformation comprising a device identifier identifying the UE, the modemfunction, the selected hypothesis and associated weights, metrics foreach feature and state information, if the state information isavailable, in response to a trigger point being met when executing themodem function.
 17. The apparatus of claim 16, wherein each hypothesisis one of linear regression, support vector machine and logisticregression.
 18. The apparatus of claim 17, wherein the selectedhypothesis includes associated neural networks information for applyingthe hypothesis.
 19. The apparatus of claim 16, wherein the one or moreprocessors are configured to execute instructions to send to the server,a modem algorithm identifier identifying a version of the set ofhypotheses stored on the UE.
 20. The apparatus of claim 16, wherein afrequented node is a network access node that has been accessed within agiven time period and a non-frequented node is a network access nodethat has not been accessed within the given time period.
 21. Theapparatus of claim 16, wherein the one or more processors are configuredto execute instructions to receive, from the server, a selected unitsourced hypothesis and a selected crowd sourced hypothesis, with eachselection corresponding to a modem function and including weights foreach feature associated with the modem function, wherein the selectedunit sourced hypothesis is based on information the UE sent to theserver and the information being run through one or more learningalgorithms and the selected crowd sourced hypothesis based on crowdsourced data.
 22. The apparatus of claim 21, wherein the one or morelearning algorithms are unsupervised learning algorithms.
 23. A serverfor wireless communications, comprising: a transceiver; a memoryconfigured to store instructions; and one or more processorscommunicatively coupled with the transceiver and the memory, wherein theone or more processors are configured to execute instructions to:receive information from a user equipment (UE), the informationcomprising a device identifier, a modem function executed by the UE, aselected hypothesis for the corresponding modem function, metrics foreach feature associated with the corresponding modem function and stateinformation, if the state information is available, in response to atrigger point associated with the modem function being met whenexecuting the modem function; store the received information; run one ormore learning algorithms based on the stored information to cluster theinformation and to select a unit sourced hypothesis and associatedweights for the corresponding modem function when the UE provided thestate information; run one or more learning algorithms based on crowdsourced information from other UEs to cluster the information and toselect a unit sourced hypothesis and associated weights for thecorresponding modem function when the state information was not providedby the UE; select the unit sourced hypothesis and associated weightsbased on the one or more learning algorithms; run one or more learningalgorithms based on crowd sourced information from other UEs to clusterthe information and to select a crowd sourced hypothesis and associatedweights for the corresponding modem function when the UE provided thestate information; select the crowd sourced hypothesis and associatedweights based on the one or more learning algorithms; and send, to theUE, at least one of the selected unit sourced hypothesis and associatedweights or the selected crowd sourced hypothesis and associated weightsfor the corresponding modem function.
 24. The server of claim 23,wherein the received information further comprises weights associatedwith the selected hypothesis for the corresponding modem function. 25.The server of claim 24, wherein the one or more processors areconfigured to execute instructions to receive, from the UE, a modemalgorithm identifier indicating a version of a set of hypotheses storedon the UE and the selected unit sourced hypothesis and the selected unitsourced hypothesis are selected from the set of hypotheses stored on theUE.
 26. The server of claim 24, wherein the one or more processors areconfigured to execute instructions to receive, from the UE, a subset ofhypotheses that the UE can use and the and the selected unit sourcedhypothesis and the selected unit sourced hypothesis are selected fromthe subset of hypotheses stored on the UE, where the subset is less thana set of hypotheses stored on the UE.
 27. The server of claim 24,wherein each hypothesis is one of linear regression, support vectormachine and logistic regression.
 28. The server of claim 27, wherein theone or more processors are configured to execute instructions to employneural networks and send neural networks information for applying aselected hypothesis.
 29. The server of claim 23, wherein the one or moreprocessors are configured to execute instructions to send, to the UE,state information associated with a selected unit sourced hypothesiswhen the server ran one or more learning algorithms based on crowdsourced information when the state information was not provided by theUE.
 30. The server of claim 23, wherein the one or more processors areconfigured to execute instructions to send, to the UE, at least one ofstate information associated with a selected unit sourced hypothesis andstate information associated with a selected crowd sourced hypothesis.